Case Data
# read in data using API from divs in html
# cdc_cases =
# GET("www.cdc.gov//coronavirus/2019-ncov/json/new-cases-chart-data.json") %>%
# content("text") %>%
# jsonlite::fromJSON() %>%
# as_tibble() %>%
# select(-V1)
# transpose the data into desired format
# cdc_trans = as_tibble(t(as.matrix(cdc_cases))) %>%
# rename(date = V1, new_cases = V2) %>%
# mutate(new_cases = as.numeric(new_cases),
# new_date = as.Date(date, format = "%m/%d/%y"))
# cdc_trans %>%
# ggplot(aes(x = new_date, y = new_cases)) +
# geom_point() + geom_line() +
# theme_classic() +
# theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
# labs(x = "Date", y = "Number of New COVID-19 Cases")
Death Data by Day and State
# read in data using API
# cdc_death =
# GET("https://data.cdc.gov/resource/r8kw-7aab.json") %>%
# content("text") %>%
# jsonlite::fromJSON() %>%
# as_tibble() %>%
# mutate(data_as_of = substr(data_as_of, 1, 10),
# start_week = substr(start_week, 1, 10),
# end_week = substr(end_week, 1, 10),
# covid_deaths = as.numeric(covid_deaths),
# total_deaths = as.numeric(total_deaths),
# percent_of_expected_deaths = as.numeric(percent_of_expected_deaths),
# pneumonia_deaths = as.numeric(pneumonia_deaths),
# pneumonia_and_covid_deaths = as.numeric(pneumonia_and_covid_deaths),
# influenza_deaths = as.numeric(influenza_deaths),
# pneumonia_influenza_or_covid = as.numeric(pneumonia_influenza_or_covid))
# name states you want to see-is this something we can do in the dashboard?
# enter up to x amount of states?
# states_select = c("New York", "New Jersey", "California", "Maryland", "Arizona", "Washington", "New Jersey")
# death_plot = cdc_death %>%
# filter(state %in% states_select) %>%
# filter(state != "United States" & state != "New York City") %>%
# ggplot(aes(x = end_week, y = covid_deaths, color = state, group = state)) +
# geom_point() + geom_line() +
# theme_classic() +
# theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
# labs(x = "Week End Date", y = "Number of COVID-19 Deaths", color = "State")
# ggplotly(death_plot)
# ggplotly(cdc_death %>%
# filter(state == "New York") %>%
# ggplot(aes(x = end_week, y = covid_deaths)) +
# geom_bar(stat = "identity", fill = "darkblue") +
# theme_classic() +
# theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
# labs(
# x = "Week Ending In",
# y = "No. COVID-19 Deaths"
# ))
Case and Death Data
# will need to figure out automating the date columns
# pivot dates to date variable instead of variable name
# mutate date variable into date format
# calculate lag difference to get new cases and deaths
# read in case data
# str_c("x", str_replace_all(substr(as.character(Sys.Date() - 1), 7, 10), "-", "_"), "_20") does not work when it comes to knitting
cases = read_csv("https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv", col_names = TRUE) %>%
janitor::clean_names() %>%
pivot_longer(x1_22_20:x5_14_20,
names_to = "date",
values_to = "total_cases") %>%
mutate(date = substr(date, 2, 8),
date = str_replace_all(date, "_", "/"),
date = as.Date(date, "%m/%d/%y")) %>%
group_by(state, date) %>%
summarize(total_cases = sum(total_cases)) %>%
group_by(state) %>%
mutate(new_cases = if_else(date != "2020-01-22", total_cases - lag(total_cases), total_cases))
# minimum new cases: -36
# maximum new cases: 13262
# read in death data
deaths = read_csv("https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_deaths_usafacts.csv", col_names = TRUE) %>%
janitor::clean_names() %>%
pivot_longer(x1_22_20:x5_14_20,
names_to = "date",
values_to = "total_deaths") %>%
mutate(date = substr(date, 2, 8),
date = str_replace_all(date, "_", "/"),
date = as.Date(date, "%m/%d/%y")) %>%
group_by(state, date) %>%
summarize(total_deaths = sum(total_deaths)) %>%
group_by(state) %>%
mutate(new_deaths = if_else(date != "2020-01-22", total_deaths - lag(total_deaths), total_deaths))
# minimum new deaths: -45
# maximum new deaths: 4564
# read in population data
pop = read_csv("https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_county_population_usafacts.csv", col_names = TRUE) %>%
janitor::clean_names() %>%
group_by(state) %>%
summarize(population = sum(population))
# join population counts to cases and deaths datasets for normalized counts
cases2 = left_join(cases, pop, by = "state") %>%
mutate(new_cases_norm = (new_cases/population)*100000)
# minimum new cases norm: -1.69
# maximum new cases norm: 83.07
deaths2 = left_join(deaths, pop, by = "state") %>%
mutate(new_deaths_norm = (new_deaths/population)*100000)
# minimum new deaths norm: -0.82
# maximum new deaths norm: 23.46
Make Plots
# these will only show one state at a time on the dashboard-will have drop down menu
# cases plot non-normal
cases_plot = cases2 %>%
mutate(text_label = str_c("Date: ", date, '\nCases: ', new_cases)) %>%
filter(state == "NY") %>%
ggplot(aes(x = date, y = new_cases)) +
geom_bar(stat = "identity", fill = "lightblue") +
theme_classic() +
labs(
x = "Date",
y = "No. New Cases",
title = "New COVID-19 Cases by Day") +
scale_x_date(date_labels = "%b %d",
date_breaks = "7 days") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
ggplotly(cases_plot)
# cases plot normal
cases_plot_norm = cases2 %>%
mutate(text_label = str_c("Date: ", date, '\nCases per 100k: ', new_cases_norm)) %>%
filter(state == "NY") %>%
ggplot(aes(x = date, y = new_cases_norm)) +
geom_bar(stat = "identity", fill = "lightblue") +
theme_classic() +
labs(
x = "Date",
y = "No. New Cases per 100,000",
title = "New COVID-19 Cases per 100,000 by Day"
) +
scale_x_date(date_labels = "%b %d",
date_breaks = "7 days") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
ggplotly(cases_plot_norm)
# death plot
death_plot = deaths2 %>%
mutate(text_label = str_c("Date: ", date, '\nDeaths: ', new_deaths)) %>%
filter(state == "NY") %>%
ggplot(aes(x = date, y = new_deaths)) +
geom_bar(stat = "identity", fill = "darkblue") +
theme_classic() +
labs(
x = "Date",
y = "No. New Deaths",
title = "New COVID-19 Deaths by Day"
) +
scale_x_date(date_labels = "%b %d",
date_breaks = "7 days") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
ggplotly(death_plot)
# death plot norm
death_plot_norm = deaths2 %>%
mutate(text_label = str_c("Date: ", date, '\nDeaths per 100k: ', new_deaths_norm)) %>%
filter(state == "NY") %>%
ggplot(aes(x = date, y = new_deaths_norm)) +
geom_bar(stat = "identity", fill = "darkblue") +
theme_classic() +
labs(
x = "Date",
y = "No. New Deaths per 100,000",
title = "New COVID-19 Deaths per 100,000 by Day"
) +
scale_x_date(date_labels = "%b %d",
date_breaks = "7 days") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
# need
ggplotly(death_plot_norm)
ggplotly(deaths2 %>%
ggplot(aes(x = date, y = new_deaths, color = state)) +
geom_point() + geom_line() +
theme_classic() +
theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
labs(x = "Date", y = "Number of COVID-19 Deaths", color = "State") +
scale_x_date(date_labels = "%b %d",
date_breaks = "7 days") +
theme(axis.text.x = element_text(angle = 60, hjust = 1)))